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Paper Abstract and Keywords
Presentation 2019-03-13 16:25
Improving the robusteness of neural networks to adversarial examples by reducing color depth of training inage data
Shuntaro Miyazato, Toshihiko Yamasaki, Kiyoharu Aizawa (UTokyo) EMM2018-109
Abstract (in Japanese) (See Japanese page) 
(in English) In this research, we propose a method to train a neural network that is robust to adversarial examples to image classification.
First, we show that the accuracy of adversarial example can be improved while keeping the accuracy of data which is not adversarial example by dropping RGB value information of the training image.
In addition, we suggest that the robustness improves further by determining the quantization level so that the loss function is maximized just before back propagation of the neural network.
Finally, we report the ensemble of the model trained with quantization accomplished the same performance as the model adversarial trained, if they can reject indeterminate examples.
Keyword (in Japanese) (See Japanese page) 
(in English) deep learning / adversarial example / / / / / /  
Reference Info. IEICE Tech. Rep., vol. 118, no. 494, EMM2018-109, pp. 95-100, March 2019.
Paper # EMM2018-109 
Date of Issue 2019-03-06 (EMM) 
ISSN Print edition: ISSN 0913-5685  Online edition: ISSN 2432-6380
Copyright
and
reproduction
All rights are reserved and no part of this publication may be reproduced or transmitted in any form or by any means, electronic or mechanical, including photocopy, recording, or any information storage and retrieval system, without permission in writing from the publisher. Notwithstanding, instructors are permitted to photocopy isolated articles for noncommercial classroom use without fee. (License No.: 10GA0019/12GB0052/13GB0056/17GB0034/18GB0034)
Download PDF EMM2018-109

Conference Information
Committee EMM  
Conference Date 2019-03-13 - 2019-03-14 
Place (in Japanese) (See Japanese page) 
Place (in English) TBD 
Topics (in Japanese) (See Japanese page) 
Topics (in English) Image and Sound Quality, Metrics for Perception and Recognition, Human Auditory and Visual System, etc. 
Paper Information
Registration To EMM 
Conference Code 2019-03-EMM 
Language Japanese 
Title (in Japanese) (See Japanese page) 
Sub Title (in Japanese) (See Japanese page) 
Title (in English) Improving the robusteness of neural networks to adversarial examples by reducing color depth of training inage data 
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Keyword(1) deep learning  
Keyword(2) adversarial example  
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1st Author's Name Shuntaro Miyazato  
1st Author's Affiliation The University of Tokyo (UTokyo)
2nd Author's Name Toshihiko Yamasaki  
2nd Author's Affiliation The University of Tokyo (UTokyo)
3rd Author's Name Kiyoharu Aizawa  
3rd Author's Affiliation The University of Tokyo (UTokyo)
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Speaker
Date Time 2019-03-13 16:25:00 
Presentation Time 25 
Registration for EMM 
Paper # IEICE-EMM2018-109 
Volume (vol) IEICE-118 
Number (no) no.494 
Page pp.95-100 
#Pages IEICE-6 
Date of Issue IEICE-EMM-2019-03-06 


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